Semantic Kernel for Enterprise AI: Architecting Production-Grade LLM Integration in .NET

Semantic Kernel for Enterprise AI: Architecting Production-Grade LLM Integration in .NET — Foundations — Part 1 This article is Part 1 of a series on Semantic Kernel for Enterprise AI in .NET. I work at the intersection of distributed systems, AI infrastructure, and .NET engineering. I. Executive Summary The Gap Between Demo and Production Every engineering team that has delivered an LLM proof-of-concept eventually faces the same humbling reality: a polished ChatGPT-style demo is light years away from a production system that handles real business transactions under real load, with real money on the line. The raw OpenAI or Azure OpenAI API gets you to the demo in days. Getting from there to a system that a Fortune 500 organization can stake its operations on — that takes an architectural framework purpose-built for the challenge. Microsoft’s Semantic Kernel is that framework for .NET engineers. At its core, Semantic Kernel is an open-source SDK that functions as an orchestration layer